deeptables.preprocessing package

Submodules

deeptables.preprocessing.transformer module

class deeptables.preprocessing.transformer.CategorizeEncoder(columns=None, remain_numeric=True)[source]

Bases: object

fit(X)[source]
fit_transform(X)[source]
transform(X)[source]
class deeptables.preprocessing.transformer.DataFrameWrapper(transform, columns=None)[source]

Bases: object

fit(X)[source]
fit_transform(X)[source]
transform(X)[source]
class deeptables.preprocessing.transformer.GaussRankScaler[source]

Bases: object

fit_transform(X)[source]
class deeptables.preprocessing.transformer.LgbmLeavesEncoder(cat_vars, cont_vars, task, **params)[source]

Bases: object

fit(X, y)[source]
fit_transform(X, y)[source]
transform(X)[source]
class deeptables.preprocessing.transformer.MultiKBinsDiscretizer(columns=None, bins=None, strategy='quantile')[source]

Bases: object

fit(X)[source]
fit_transform(X)[source]
transform(X)[source]
class deeptables.preprocessing.transformer.MultiLabelEncoder(columns=None)[source]

Bases: object

fit(X)[source]
fit_transform(X)[source]
transform(X)[source]
class deeptables.preprocessing.transformer.MultiVarLenFeatureEncoder(features)[source]

Bases: object

fit(X)[source]
fit_transform(X)[source]
transform(X)[source]
class deeptables.preprocessing.transformer.PassThroughEstimator[source]

Bases: object

fit(X)[source]
fit_transform(X)[source]
transform(X)[source]
class deeptables.preprocessing.transformer.SafeLabelEncoder[source]

Bases: sklearn.preprocessing._label.LabelEncoder

transform(y)[source]

Transform labels to normalized encoding.

Parameters:y (array-like of shape (n_samples,)) – Target values.
Returns:y
Return type:array-like of shape (n_samples,)
class deeptables.preprocessing.transformer.VarLenFeatureEncoder(sep='|')[source]

Bases: object

fit(X: pandas.core.series.Series)[source]
max_element_length
n_classes
transform(X: pandas.core.series.Series)[source]

deeptables.preprocessing.utils module

deeptables.preprocessing.utils.target_encoding(train, target, test=None, feat_to_encode=None, smooth=0.2, random_state=9527)[source]
deeptables.preprocessing.utils.target_rate_encodeing(feat_to_encode, target, df, mode='order')[source]

Module contents